This is a video action recognition model fine-tuned on the WLASL dataset based on the VideoMAE-Base architecture. After 200 epochs of training, it achieved a top-1 accuracy of 52.96% and a top-5 accuracy of 79.88% on the evaluation set, specifically designed for sign language action recognition tasks.

Computer VisionTransformers

This model is a fine-tuned version based on meta-llama/Llama-3.3-70B-Instruct. It is trained using the mlabonne/FineTome-100k dataset, which contains 100k token data. The model is fine-tuned using the Unsloth and Huggingface TRL libraries and supports English language processing.

Natural Language ProcessingTransformersEnglish

This project is based on the moonshotai/Kimi-K2-Thinking base model. The original INT4 model is converted into a higher-quality text generation model through a custom anti-quantization script, aiming to make knowledge accessible to everyone.

Natural Language ProcessingGguf

The top model in the aquif-3.5 series, with advanced inference capabilities and a large context window of 1 million tokens, and has excellent performance in multiple benchmark tests. The comprehensive AAII score reaches 60 points.

Natural Language ProcessingTransformersMultiple Languages

A text generation model developed based on the MLX library, focusing on natural language processing tasks and providing developers with an efficient text generation solution.

Natural Language ProcessingMlxEnglish

A text generation model implemented based on the MLX library, supporting inference in multiple quantization methods, with distributed computing capabilities, and can run efficiently in the Apple hardware environment.

Natural Language ProcessingMlxEnglish

Technically Color WAN 2.2 is a LoRA model designed specifically for generating classic movie-style images. Through vivid color palettes, rich saturation, and dramatic lighting effects, the generated images have the unique visual charm of classic movies.

Computer VisionDiffusers

Kimi-K2-Thinking is a large language model in MLX format converted by mlx-community from the original model of moonshotai. The conversion is carried out using version 0.28.4 of mlx-lm, retaining the chain of thought reasoning ability of the original model.

Natural Language ProcessingMlx

This is an enhanced multimodal vision-language model based on the Qwen3-VL-8B-Thinking model. It is extended to 12B parameters through Brainstorm 20x technology and uses NEO Imatrix-enhanced GGUF quantization. The model has powerful image understanding, text generation, and multimodal reasoning capabilities, with significant improvements in visual perception, text quality, and creative scenarios.

MultimodalGgufMultiple Languages

MM-VAE Lyra is a multimodal variational autoencoder specifically designed for text embedding conversion, using geometric fusion technology. It combines the CLIP-L and T5-base models and can effectively handle the encoding and decoding tasks of text embeddings, providing an innovative solution for multimodal data processing.

This is the GGUF quantized version of the Qwen/Qwen3-Coder-30B-A3B-Instruct language model, specifically optimized for code generation and programming tasks. It is converted using FP32 precision and is suitable for local inference deployment.

Natural Language ProcessingGgufMultiple Languages

This is a text-to-speech model optimized based on the MLX framework, converted from the original model Marvis-AI/marvis-tts-100m-v0.2. It uses 6-bit quantization technology and is specifically optimized for Apple Silicon hardware, providing efficient speech synthesis capabilities.

Audio ProcessingTransformersMultiple Languages

This is the MXFP4_MOE quantized version of the MiniMax-M2-THRIFT model. It has been compressed based on the original model, including 25% expert pruning (from 256 to 192) and setting top_k=8. Meanwhile, it retains the characteristics of the encoding model and can be used for text generation tasks.

Natural Language ProcessingGguf

This is a 🤗 Transformers model published on the Hugging Face Hub. Specific information needs to be obtained from the model page. This model is based on the Transformer architecture and is suitable for various natural language processing tasks.

Natural Language ProcessingTransformers

The aquif-3.5 series is the masterpiece released on November 3, 2025, including two versions, Plus and Max, which provide advanced inference capabilities and an unprecedented 1 million token context window, achieving state-of-the-art performance in their respective categories.

Natural Language ProcessingTransformersMultiple Languages

This is a quantized version model based on Moonshot AI technology, focusing on visual language understanding and generation tasks, and committed to lowering the threshold of knowledge acquisition and making knowledge accessible to everyone.

Natural Language ProcessingGguf

nutrient-gram-qwen-3-vl-2b is a fine-tuned version based on Qwen/Qwen3-VL-2B-Instruct, trained using TRL for supervised fine-tuning, mainly used for text generation tasks. This model is optimized for specific tasks while maintaining its original multimodal capabilities.

Natural Language ProcessingTransformers

A static quantized version of UME-R1-7B, supporting multiple tasks such as sentence similarity, embedding, zero-shot image classification, and video text-to-text. It offers various quantization types to meet different needs, from the lightweight Q2_K to the high-quality Q8_0 version.

MultimodalTransformersEnglish

Qwen3-Coder-REAP-363B-A35B is a sparse mixture-of-experts model obtained by pruning 25% of the experts from Qwen3-Coder-480B-A35B-Instruct using the REAP method. While maintaining performance close to the original model, it significantly reduces the parameter scale and memory requirements, making it particularly suitable for code generation and intelligent coding scenarios with limited resources.

Natural Language ProcessingTransformersEnglish

NV-Reason-CXR-3B GGUF is a quantized version of the NVIDIA NV-Reason-CXR-3B vision-language model, optimized for edge device deployment. This is a model with 3 billion parameters, focusing on chest X-ray analysis. It has been converted to the GGUF format and quantized for efficient operation on mobile devices, desktops, and embedded systems.

MultimodalGgufEnglish